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4 papers
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations
Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis
We consider strongly-nonlinear and weakly-dispersive surface water waves governed by equations of Boussinesq type, known as the Serre-Green-Naghdi system; it describes future state…
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi +1
We propose a new type of neural networks, Kronecker neural networks (KNNs), that form a general framework for neural networks with adaptive activation functions. KNNs employ the Kr…
Parallel Physics-Informed Neural Networks via Domain Decomposition
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
We develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs),…
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Ameya D. Jagtap, George Em Karniadakis
We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions o…